{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Multi-Linear_Regression.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyMxGEgDoTTu9rFFXQPG5ZLK",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Divyanshu-ISM/Machine-Learning-Deep-Learning/blob/main/Multi_Linear_Regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "o6t8qvQEK8wy"
      },
      "source": [
        "# Practicing Regression and Hands on Feature Engineering. \n",
        "\n",
        "> Divyanshu Vyas | Oil and Gas Data Science"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "5g2KoLpJgj4j"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2DSDsRZChiF4"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Multiple-Linear-Regression/master/50_Startups.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "_3nTO_BVh0J_",
        "outputId": "6aad7efa-f879-496b-be29-bc18004d6b69"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>State</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>New York</td>\n",
              "      <td>192261.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>California</td>\n",
              "      <td>191792.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>Florida</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>New York</td>\n",
              "      <td>182901.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>Florida</td>\n",
              "      <td>166187.94</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend       State     Profit\n",
              "0  165349.20       136897.80        471784.10    New York  192261.83\n",
              "1  162597.70       151377.59        443898.53  California  191792.06\n",
              "2  153441.51       101145.55        407934.54     Florida  191050.39\n",
              "3  144372.41       118671.85        383199.62    New York  182901.99\n",
              "4  142107.34        91391.77        366168.42     Florida  166187.94"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6xAWNMdGmbgX",
        "outputId": "c787e95c-a74b-4a31-e022-712bab0357f8"
      },
      "source": [
        "len(df)"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "50"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R7ARKgQqmdRM"
      },
      "source": [
        "x1 = df['R&D Spend']\n",
        "x2 = df['Administration']\n",
        "x3 = df['Marketing Spend']\n",
        "x_4 = df['State']\n",
        "y = df['Profit']"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 417
        },
        "id": "Gz66c7tZmkcx",
        "outputId": "b2910df4-fb86-4c60-da8c-ff13936d9abd"
      },
      "source": [
        "sns.regplot(x1,y)\n",
        "sns.regplot(x2,y)\n",
        "sns.regplot(x3,y)"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n",
            "/usr/local/lib/python3.6/dist-packages/seaborn/_decorators.py:43: FutureWarning: Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.\n",
            "  FutureWarning\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f026ceb8fd0>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "V6AiaMcynAmJ",
        "outputId": "d83a2775-6c41-4d5d-8a2f-89388f0cd537"
      },
      "source": [
        "plt.style.use('default')\n",
        "sns.pairplot(df)"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<seaborn.axisgrid.PairGrid at 0x7f0268d54828>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1000x1000 with 20 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Lrg1dcSHnKZw",
        "outputId": "2d44c84f-e2f4-4ea2-872f-1102c2250f21"
      },
      "source": [
        "set(df['State'])"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'California', 'Florida', 'New York'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 470
        },
        "id": "NS8P0_T4nkxQ",
        "outputId": "112480c0-d5e4-4904-ccd2-b5406bdea85c"
      },
      "source": [
        "sns.boxplot(x='State',y='Profit',data=df)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0268dec940>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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mKjQ01Ok2AAAO4/QfAACADThShfPi8XiUmprqaA/5+fnq1auXJGnx4sXyeDyO9uP0+gEAlQOhCufF5XJVqlNdHo+nUvUDAKi6OP0HAABgA0IVAACADQhVAAAANiBUAQAA2IBQBQAAYANCFQAAgA0cDVWrV6/W3XffraioKLlcLi1atMhv/NFHH5XL5fKbunfv7lfz008/acCAAQoLC1Pt2rU1aNAgHTt2zK9m48aN6tixozwej6KjozVp0qQyvSxcuFBNmzaVx+NRy5YttWzZMr9xY4zGjh2rhg0bKjQ0VAkJCdq+fbtNWwIAAAQ6R0PV8ePH1bp1a82ePfuMNd27d9eBAwes6b//+7/9xgcMGKDNmzcrLS1NS5Ys0erVqzVkyBBr3OfzqVu3boqJiVFmZqYmT56s8ePH680337Rq1q5dq/79+2vQoEH65ptv1Lt3b/Xu3VubNm2yaiZNmqSZM2dqzpw5WrdunWrWrCmv1+v49+ABAIDKwdGbf/bo0UM9evQ4a43b7VZkZGS5Y1u2bNHy5cu1fv163XbbbZKk119/XXfddZdee+01RUVFKSUlRYWFhZo7d65CQkLUvHlzZWVlaerUqVb4mjFjhrp3766RI0dKkl5++WWlpaVp1qxZmjNnjowxmj59usaMGWPdyfudd95RRESEFi1apH79+tm1SQAAQICq9NdUrVy5Ug0aNFCTJk00dOhQHT582BrLyMhQ7dq1rUAlSQkJCQoKCtK6deusmk6dOikkJMSq8Xq92rZtm44cOWLVJCQk+K3X6/UqIyNDkrRr1y5lZ2f71YSHhysuLs6qKU9BQYF8Pp/fBAAArkyVOlR1795d77zzjtLT0/Xqq69q1apV6tGjh4qLiyVJ2dnZatCggd9zqlWrprp16yo7O9uqiYiI8KspfXyumtPHT39eeTXlSU5OVnh4uDVFR0ef1+sHAACBo1J/99/pp9VatmypVq1a6frrr9fKlSvVtWtXBzurmNGjRyspKcl67PP5CFYAAFyhKvWRql+67rrrVL9+fe3YsUOSFBkZqYMHD/rVnDx5Uj/99JN1HVZkZKRycnL8akofn6vm9PHTn1deTXncbrfCwsL8JgAAcGUKqFC1b98+HT58WA0bNpQkxcfH6+jRo8rMzLRqPvvsM5WUlCguLs6qWb16tYqKiqyatLQ0NWnSRHXq1LFq0tPT/daVlpam+Ph4SVJsbKwiIyP9anw+n9atW2fVAACAqs3RUHXs2DFlZWUpKytL0qkLwrOysrR3714dO3ZMI0eO1Jdffqndu3crPT1dvXr10g033CCv1ytJuvnmm9W9e3cNHjxYX331ldasWaNhw4apX79+ioqKkiQ9+OCDCgkJ0aBBg7R582Z98MEHmjFjht9pueeee07Lly/XlClTtHXrVo0fP15ff/21hg0bJklyuVwaPny4XnnlFX388cf67rvv9MgjjygqKkq9e/e+zFsNAABUSsZBK1asMJLKTAMHDjR5eXmmW7du5uqrrzbVq1c3MTExZvDgwSY7O9tvGYcPHzb9+/c3V111lQkLCzOPPfaY+fnnn/1qvv32W3PHHXcYt9ttrrnmGjNx4sQyvXz44YfmpptuMiEhIaZ58+Zm6dKlfuMlJSXmxRdfNBEREcbtdpuuXbuabdu2ndfrzc3NNZJMbm7ueT0P/vLy8kzHjh1Nx44dTV5entPtAOyTqHTYJ+1V0fdvlzHGOJjpqhSfz6fw8HDl5uZyfdVFOHHihHW0MjU1VaGhoQ53hKqOfRKVDfukvSr6/h1Q11QBAABUVoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaV+mtqAAA4H8YY5efnO92G407fBmyPUzwej1wu1yVdB6EKAHDFyM/Pt24lgFN69erldAuVwuW4tQSn/wAAAGzAkSoAwBWp+O7iqvsuZyQV/9/PwZIu7VmvyuukFPyP4Mu2uqq6uwEArnTVVLXf5ao73UDVw+k/AAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwQVX+ViQAF8kYo/z8fKfbcNzp24DtcYrH45HLVVW/xRdVFaEKwAXLz8+X1+t1uo1KpVevXk63UCmkpqYqNDTU6TaAy4rTfwAAADbgSBUAW8zudFTuYON0G44wRiosOfVzSJBUVc96FRS7lLi6ttNtAI4hVAGwhTvYyBPsdBfO4USXJFXNUA2U4vQfAACADQhVAAAANiBUAQAA2IBrqgIE9wP6N+4JVBb3BAIA5xGqAgT3Ayof9wQ6hXsCAYDzOP0HAABgA45UBaDjtw6QgqrwP50xUsnJUz8HVau6NwUqOamaG1Kc7gIA8H+q8DtzAAuqJgVXd7oLh4U43QAAAH44/QcAAGADQhUAAIANHA1Vq1ev1t13362oqCi5XC4tWrTIGisqKtKoUaPUsmVL1axZU1FRUXrkkUe0f/9+v2Vce+21crlcftPEiRP9ajZu3KiOHTvK4/EoOjpakyZNKtPLwoUL1bRpU3k8HrVs2VLLli3zGzfGaOzYsWrYsKFCQ0OVkJCg7du327g1AABAIHM0VB0/flytW7fW7Nmzy4zl5eVpw4YNevHFF7VhwwZ99NFH2rZtm+65554ytS+99JIOHDhgTc8884w15vP51K1bN8XExCgzM1OTJ0/W+PHj9eabb1o1a9euVf/+/TVo0CB988036t27t3r37q1NmzZZNZMmTdLMmTM1Z84crVu3TjVr1pTX6+U+SQAAQJLDF6r36NFDPXr0KHcsPDxcaWlpfvNmzZql9u3ba+/evWrcuLE1v1atWoqMjCx3OSkpKSosLNTcuXMVEhKi5s2bKysrS1OnTtWQIUMkSTNmzFD37t01cuRISdLLL7+stLQ0zZo1S3PmzJExRtOnT9eYMWOs+yK98847ioiI0KJFi9SvX7+L3hYAACCwBdQ1Vbm5uXK5XKpdu7bf/IkTJ6pevXq65ZZbNHnyZJ08edIay8jIUKdOnRQS8u9Pi3m9Xm3btk1HjhyxahISEvyW6fV6lZGRIUnatWuXsrOz/WrCw8MVFxdn1ZSnoKBAPp/PbwIAAFemgLmlQn5+vkaNGqX+/fsrLCzMmv/ss8/q1ltvVd26dbV27VqNHj1aBw4c0NSpUyVJ2dnZio2N9VtWRESENVanTh1lZ2db806vyc7OtupOf155NeVJTk7WhAkTLvAVAwCAQBIQoaqoqEj/9V//JWOM/vjHP/qNJSUlWT+3atVKISEhevLJJ5WcnCy32325W/UzevRov/58Pp+io6Md7AgAAFwqlf70X2mg2rNnj9LS0vyOUpUnLi5OJ0+e1O7duyVJkZGRysnJ8aspfVx6HdaZak4fP/155dWUx+12KywszG8CAABXpkodqkoD1fbt2/Xpp5+qXr1653xOVlaWgoKC1KBBA0lSfHy8Vq9eraKiIqsmLS1NTZo0UZ06daya9PR0v+WkpaUpPj5ekhQbG6vIyEi/Gp/Pp3Xr1lk1AACganP09N+xY8e0Y8cO6/GuXbuUlZWlunXrqmHDhrr//vu1YcMGLVmyRMXFxdb1S3Xr1lVISIgyMjK0bt06denSRbVq1VJGRoZGjBihhx56yApMDz74oCZMmKBBgwZp1KhR2rRpk2bMmKFp06ZZ633uuefUuXNnTZkyRT179tT777+vr7/+2rrtgsvl0vDhw/XKK6/oxhtvVGxsrF588UVFRUWpd+/el3GLAQCAysrRUPX111+rS5cu1uPS648GDhyo8ePH6+OPP5YktWnTxu95K1as0J133im32633339f48ePV0FBgWJjYzVixAi/65jCw8P1ySefKDExUW3btlX9+vU1duxY63YKkvSrX/1K7733nsaMGaPf/e53uvHGG7Vo0SK1aNHCqnnhhRd0/PhxDRkyREePHtUdd9yh5cuXy+PxXJJtAwAAAoujoerOO++UMeaM42cbk6Rbb71VX3755TnX06pVK33++ednrenbt6/69u17xnGXy6WXXnpJL7300jnXBwAAqp5KfU0VAABAoCBUAQAA2IBQBQAAYANCFQAAgA0IVQAAADYgVAEAANiAUAUAAGADQhUAAIANCFUAAAA2IFQBAADYgFAFAABgA0IVAACADQhVAAAANiBUAQAA2IBQBQAAYANCFQAAgA0IVQAAADYgVAEAANjggkLV3r17ZYwpM98Yo7179150UwAAAIHmgkJVbGysDh06VGb+Tz/9pNjY2ItuCgAAINBUu5AnGWPkcrnKzD927Jg8Hs9FNwUAwIXwO4ty0rk+UEmctg+Ud4bNbucVqpKSkiRJLpdLL774omrUqGGNFRcXa926dWrTpo29HQIAUEEFBQXWz8H/CHawE1Q2BQUFfrnlUjivUPXNN99IOpX2vvvuO4WEhFhjISEhat26tX7zm9/Y2yEk/SJhFxc51wgqj9P2g8vxFxgA4OzOK1StWLFCkvTYY49pxowZCgsLuyRNoazT//qq+c17DnaCyuhy/AUGBAK32239XHx38QVe5IIrxsl/H7E8fd+4VC5od5s3b57dfQAAcNH8rvetJkIVLOVdC263Cu9u9913n+bPn6+wsDDdd999Z6396KOPLrox+Ds9YR+/5UEpuLqD3aBSKC6yjlpejr/AAABnV+FQFR4ebqW8sLCwy5L48G9+2zu4OqEKfvj/EQCcV+FQde+991q3S5g/f/6l6gcAACAgVfjmn/fee6+OHj0qSQoODtbBgwcvWVMAAACBpsKh6uqrr9aXX34p6cw3/wQAAKiqKnz676mnnlKvXr3kcrnkcrkUGRl5xtri4mJbmgMAAAgUFQ5V48ePV79+/bRjxw7dc889mjdvnmrXrn0pewMAAAgY53UHj6ZNm6pp06YaN26c+vbty80GAQAA/s8F3RZt3LhxkqRDhw5p27ZtkqQmTZro6quvtq8zAACAAFLhC9VPl5eXp8cff1xRUVHq1KmTOnXqpKioKA0aNEh5eXl29wgAAFDpXVCoGjFihFatWqWPP/5YR48e1dGjR7V48WKtWrVKzz//vN09AgAAVHoXFKr+9re/6c9//rN69OihsLAwhYWF6a677tJbb72lv/71rxVezurVq3X33XcrKipKLpdLixYt8hs3xmjs2LFq2LChQkNDlZCQoO3bt/vV/PTTTxowYIDCwsJUu3ZtDRo0SMeOHfOr2bhxozp27CiPx6Po6GhNmjSpTC8LFy5U06ZN5fF41LJlSy1btuy8ewEAAFXXBZ/+i4iIKDO/QYMG53X67/jx42rdurVmz55d7vikSZM0c+ZMzZkzR+vWrVPNmjXl9XqVn59v1QwYMECbN29WWlqalixZotWrV2vIkCHWuM/nU7du3RQTE6PMzExNnjxZ48eP15tvvmnVrF27Vv3799egQYP0zTffqHfv3urdu7c2bdp0Xr0AAICq64JCVXx8vMaNG+cXKE6cOKEJEyYoPj6+wsvp0aOHXnnlFd17771lxowxmj59usaMGaNevXqpVatWeuedd7R//37riNaWLVu0fPlyvf3224qLi9Mdd9yh119/Xe+//772798vSUpJSVFhYaHmzp2r5s2bq1+/fnr22Wc1depUa10zZsxQ9+7dNXLkSN188816+eWXdeutt2rWrFkV7gUAAFRtFxSqpk+frjVr1qhRo0bq2rWrunbtqujoaK1du1YzZsywpbFdu3YpOztbCQkJ1rzw8HDFxcUpIyNDkpSRkaHatWvrtttus2oSEhIUFBSkdevWWTWdOnVSSEiIVeP1erVt2zYdOXLEqjl9PaU1peupSC/lKSgokM/n85sAAMCV6YJuqdCyZUtt375dKSkp2rp1qySpf//+GjBggEJDQ21pLDs7W5LKnGaMiIiwxrKzs9WgQQO/8WrVqqlu3bp+NbGxsWWWUTpWp04dZWdnn3M95+qlPMnJyZowYcK5XywAwH4nnW7AQUZS6ZebBEuqqt8sd5n3gfMOVUVFRWratKmWLFmiwYMHX4qerhijR49WUlKS9djn8yk6OtrBjgCg6gj+R7DTLaCKOe/Tf9WrV78sF2eXfrdgTk6O3/ycnBxrLDIyUpAErXMAACAASURBVAcPHvQbP3nypH766Se/mvKWcfo6zlRz+vi5eimP2+22Ph1ZOgEAgCvTBZ3+S0xM1Kuvvqq3335b1apd0CLOKTY2VpGRkUpPT1ebNm0knTrSs27dOg0dOlTSqQvmjx49qszMTLVt21aS9Nlnn6mkpERxcXFWzf/7f/9PRUVFql69uiQpLS1NTZo0UZ06daya9PR0DR8+3Fp/WlqaddF9RXoBqiJjjPVzAd+jXuWdvg+cvm9cTh6PR6mpqY6suzLJz89Xr169JEmLFy+Wx+NxuCPnXY5tcEGJaP369UpPT9cnn3yili1bqmbNmn7jH330UYWWc+zYMe3YscN6vGvXLmVlZalu3bpq3Lixhg8frldeeUU33nijYmNj9eKLLyoqKkq9e/eWJN18883q3r27Bg8erDlz5qioqEjDhg1Tv379FBUVJUl68MEHNWHCBA0aNEijRo3Spk2bNGPGDE2bNs1a73PPPafOnTtrypQp6tmzp95//319/fXX1m0XXC7XOXsBqqKCggLr58TVdRzsBJVNQUGBI98P63K5bLu290rh8XjYJpfJBYWq2rVrq0+fPhe98q+//lpdunSxHpdefzRw4EDNnz9fL7zwgo4fP64hQ4bo6NGjuuOOO7R8+XK/tJmSkqJhw4apa9euCgoKUp8+fTRz5kxrPDw8XJ988okSExPVtm1b1a9fX2PHjvW7l9WvfvUrvffeexozZox+97vf6cYbb9SiRYvUokULq6YivVw2JVX56ktJxvx7GwRVk1xV9ArMqr4fAEAl4zLncYy2pKREkydP1scff6zCwkL9x3/8h8aPH08CriCfz6fw8HDl5uae9/VVJ06ckNfrvUSdIdClpqY68v9hXl6eunfvLkma3emI3FwXXKUVFP/7iOXy5csdOVKFU05/z3Dq98OVpKLv3+d1pOr3v/+9xo8fr4SEBIWGhmrmzJk6dOiQ5s6de9ENAwg8rtOOErqDJQ+hCv/HVVWPIKNKO69Q9c477+iNN97Qk08+KUn69NNP1bNnT7399tsKCrqg+4iigrj48t+4ALMstgEAOO+8QtXevXt11113WY8TEhLkcrm0f/9+NWrUyPbm8G9cfFk+LsAEAFQW53V46eTJk2X+Iq5evbqKiopsbQoAACDQnNeRKmOMHn30Ubndbmtefn6+nnrqKb/bKlT0lgoAAABXivMKVQMHDiwz76GHHrKtGQAAgEB1XqFq3rx5l6oPAACAgMZH9gAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALBBNacbAHBlKCh2STJOt+EIY6TCklM/hwRJLpez/Tjl1D4AVF2EKgC2SFxd2+kWAMBRnP4DAACwAUeqAFwwj8ej1NRUp9twXH5+vnr16iVJWrx4sTwej8MdOY9tgKqo0oeqa6+9Vnv27Ckz/+mnn9bs2bN15513atWqVX5jTz75pObMmWM93rt3r4YOHaoVK1boqquu0sCBA5WcnKxq1f798leuXKmkpCRt3rxZ0dHRGjNmjB599FG/5c6ePVuTJ09Wdna2Wrdurddff13t27e39wUDAcTlcik0NNTpNioVj8fDNgGqqEp/+m/9+vU6cOCANaWlpUmS+vbta9UMHjzYr2bSpEnWWHFxsXr27KnCwkKtXbtWCxYs0Pz58zV27FirZteuXerZs6e6dOmirKwsDR8+XE888YTfX+AffPCBkpKSNG7cOG3YsEGtW7eW1+vVwYMHL8NWAAAAlV2lD1VXX321IiMjrWnJkiW6/vrr1blzZ6umRo0afjVhYWHW2CeffKLvv/9e7777rtq0aaMePXro5Zdf1uzZs1VYWChJmjNnjmJjYzVlyhTdfPPNGjZsmO6//35NmzbNWs7UqVM1ePBgPfbYY2rWrJnmzJmjGjVqaO7cuZdvYwAAgEqr0oeq0xUWFurdd9/V448/Ltdpn1lOSUlR/fr11aJFC40ePVp5eXnWWEZGhlq2bKmIiAhrntfrlc/n0+bNm62ahIQEv3V5vV5lZGRY683MzPSrCQoKUkJCglVTnoKCAvl8Pr8JAABcmSr9NVWnW7RokY4ePep3rdODDz6omJgYRUVFaePGjRo1apS2bdumjz76SJKUnZ3tF6gkWY+zs7PPWuPz+XTixAkdOXJExcXF5dZs3br1jP0mJydrwoQJF/x6AQBA4AioUPXnP/9ZPXr0UFRUlDVvyJAh1s8tW7ZUw4YN1bVrV+3cuVPXX3+9E21aRo8eraSkJOuxz+dTdHS0gx0BAIBLJWBC1Z49e/Tpp59aR6DOJC4uTpK0Y8cOXX/99YqMjNRXX33lV5OTkyNJioyMtP5bOu/0mrCwMIWGhio4OFjBwcHl1pQuozxut1tut7tiLxAAAAS0gLmmat68eWrQoIF69ux51rqsrCxJUsOGDSVJ8fHx+u677/w+pZeWlqawsDA1a9bMqklPT/dbTlpamuLj4yVJISEhatu2rV9NSUmJ0tPTrRoAAFC1BUSoKikp0bx58zRw4EC/e0vt3LlTL7/8sjIzM7V79259/PHHeuSRR9SpUye1atVKktStWzc1a9ZMDz/8sL799lulpqZqzJgxSkxMtI4iPfXUU/rhhx/0wgsvaOvWrXrjjTf04YcfasSIEda6kpKS9NZbb2nBggXasmWLhg4dquPHj+uxxx67vBsDAABUSgFx+u/TTz/V3r179fjjj/vNDwkJ0aeffqrp06fr+PHjio6OVp8+fTRmzBirJjg4WEuWLNHQoUMVHx+vmjVrauDAgXrppZesmtjYWC1dulQjRozQjBkz1KhRI7399tvyer1WzQMPPKBDhw5p7Nixys7OVps2bbR8+fIyF68DAICqKSBCVbdu3WSMKTM/Ojq6zN3UyxMTE6Nly5adtebOO+/UN998c9aaYcOGadiwYedcHwAAqHoC4vQfAABAZUeoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwQUB8TQ0qD2OM8vPzHe3h9PU73YskeTweuVwup9sAADiMUIXzkp+f7/dF007r1auX0y0oNTVVoaGhTrcBAHAYp/8AAABswJEqnBePx6PU1FRHezDGqKCgQJLkdrsdP/Xm8XgcXT8AoHIgVOG8uFyuSnGqq0aNGk63AACAH07/AQAA2IBQBQAAYANCFQAAgA0IVQAAADYgVAEAANiAUAUAAGADQhUAAIANCFUAAAA2IFQBAADYgFAFAABgA0IVAACADQhVAAAANiBUAQAA2IBQBQAAYANCFQAAgA0IVQAAADYgVAEAANiAUAUAAGADQhUAAIANCFUAAAA2IFQBAADYoFKHqvHjx8vlcvlNTZs2tcbz8/OVmJioevXq6aqrrlKfPn2Uk5Pjt4y9e/eqZ8+eqlGjhho0aKCRI0fq5MmTfjUrV67UrbfeKrfbrRtuuEHz588v08vs2bN17bXXyuPxKC4uTl999dUlec0AACAwVepQJUnNmzfXgQMHrOmLL76wxkaMGKF//OMfWrhwoVatWqX9+/frvvvus8aLi4vVs2dPFRYWau3atVqwYIHmz5+vsWPHWjW7du1Sz5491aVLF2VlZWn48OF64oknlJqaatV88MEHSkpK0rhx47Rhwwa1bt1aXq9XBw8evDwbAQAAVH6mEhs3bpxp3bp1uWNHjx411atXNwsXLrTmbdmyxUgyGRkZxhhjli1bZoKCgkx2drZV88c//tGEhYWZgoICY4wxL7zwgmnevLnfsh944AHj9Xqtx+3btzeJiYnW4+LiYhMVFWWSk5PP6/Xk5uYaSSY3N/e8ngegcsvLyzMdO3Y0HTt2NHl5eU63A7BP2qyi79/VHM5057R9+3ZFRUXJ4/EoPj5eycnJaty4sTIzM1VUVKSEhASrtmnTpmrcuLEyMjJ0++23KyMjQy1btlRERIRV4/V6NXToUG3evFm33HKLMjIy/JZRWjN8+HBJUmFhoTIzMzV69GhrPCgoSAkJCcrIyDhr7wUFBSooKLAe+3y+i9oWAIDKzxij/Px8R3s4ff1O9yJJHo9HLpfL6TYuuUodquLi4jR//nw1adJEBw4c0IQJE9SxY0dt2rRJ2dnZCgkJUe3atf2eExERoezsbElSdna2X6AqHS8dO1uNz+fTiRMndOTIERUXF5dbs3Xr1rP2n5ycrAkTJpz/CwcABKz8/Hx5vV6n27D06tXL6RaUmpqq0NBQp9u45Cp1qOrRo4f1c6tWrRQXF6eYmBh9+OGHAfGPM3r0aCUlJVmPfT6foqOjHewIAABcKpU6VP1S7dq1ddNNN2nHjh36z//8TxUWFuro0aN+R6tycnIUGRkpSYqMjCzzKb3STweeXvPLTwzm5OQoLCxMoaGhCg4OVnBwcLk1pcs4E7fbLbfbfWEvFgAQkDwej9+HnZxgjLEuP3G73Y6fevN4PI6u/3IJqFB17Ngx7dy5Uw8//LDatm2r6tWrKz09XX369JEkbdu2TXv37lV8fLwkKT4+Xr///e918OBBNWjQQJKUlpamsLAwNWvWzKpZtmyZ33rS0tKsZYSEhKht27ZKT09X7969JUklJSVKT0/XsGHDLsvrBgAEDpfLVSnOptSoUcPpFqqcSn1Lhd/85jdatWqVdu/erbVr1+ree+9VcHCw+vfvr/DwcA0aNEhJSUlasWKFMjMz9dhjjyk+Pl633367JKlbt25q1qyZHn74YX377bdKTU3VmDFjlJiYaB1Beuqpp/TDDz/ohRde0NatW/XGG2/oww8/1IgRI6w+kpKS9NZbb2nBggXasmWLhg4dquPHj+uxxx5zZLsAAIDKp1Ifqdq3b5/69++vw4cP6+qrr9Ydd9yhL7/8UldffbUkadq0aQoKClKfPn1UUFAgr9erN954w3p+cHCwlixZoqFDhyo+Pl41a9bUwIED9dJLL1k1sbGxWrp0qUaMGKEZM2aoUaNGevvtt/0uMnzggQd06NAhjR07VtnZ2WrTpo2WL19e5uJ1AABQdbmMMcbpJqoKn8+n8PBw5ebmKiwszOl2ANjkxIkT1h9iVeVTTkBVUtH370p9+g8AACBQEKoAAABsQKgCAACwAaEKAADABoQqAAAAGxCqAAAAbECoAgAAsAGhCgAAwAaEKgAAABsQqgAAAGxAqAIAALABoQoAAMAGhCoAAAAbEKoAAABsQKgCAACwAaEKAWnNmjXq27ev1qxZ43QrAABIIlQhAOXn52vKlCnKycnRlClTlJ+f73RLAAAQqhB43n33XR0+fFiSdPjwYaWkpDjcEQAAhCoEmH379iklJUXGGEmSMUYpKSnat2+fw50BAKo6QhUChjFG06ZNO+P80qAFAIATCFUIGHv27NH69etVXFzsN7+4uFjr16/Xnj17HOoMAABCFQJITEyM2rVrp+DgYL/5wcHBat++vWJiYhzqDAAAQhUCiMvl0ogRI8443+VyOdAVAACnEKoQUBo1aqQBAwZYAcrlcmnAgAG65pprHO4MAFDVEaoQcB566CHVq1dPklS/fn0NGDDA4Y4AACBUIQB5PB49//zzioiIUFJSkjwej9MtAQCgak43AFyIDh06qEOHDk63AQCAhSNVAAAANiBUAQAA2IBQBQAAYANCFQAAgA0IVQAAADYgVAEAANiAUAUAAGADQhUAAIANCFUAAAA2qNShKjk5We3atVOtWrXUoEED9e7dW9u2bfOrufPOO+Vyufymp556yq9m79696tmzp2rUqKEGDRpo5MiROnnypF/NypUrdeutt8rtduuGG27Q/Pnzy/Qze/ZsXXvttfJ4PIqLi9NXX31l+2sGAACBqVKHqlWrVikxMVFffvml0tLSVFRUpG7duun48eN+dYMHD9aBAwesadKkSdZYcXGxevbsqcLCQq1du1YLFizQ/PnzNXbsWKtm165d6tmzp7p06aKsrCwNHz5cTzzxhFJTU62aDz74QElJSRo3bpw2bNig1q1by+v16uDBg5d+QwAAgErPZYwxTjdRUYcOHVKDBg20atUqderUSdKpI1Vt2rTR9OnTy33O//zP/+jXv/619u/fr4iICEnSnDlzNGrUKB06dEghISEaNWqUli5dqk2bNlnP69evn44eParly5dLkuLi4tSuXTvNmjVLklRSUqLo6Gg988wz+u1vf1uh/n0+n8LDw5Wbm6uwsLAL3g4AKpcTJ07I6/VKklJTUxUaGupwRwDsVNH370p9pOqXcnNzJUl169b1m5+SkqL69eurRYsWGj16tPLy8qyxjIwMtWzZ0gpUkuT1euXz+bR582arJiEhwW+ZXq9XGRkZkqTCwkJlZmb61QQFBSkhIcGqKU9BQYF8Pp/fBAAArkzVnG6gokpKSjR8+HB16NBBLVq0sOY/+OCDiomJUVRUlDZu3KhRo0Zp27Zt+uijjyRJ2dnZfoFKkvU4Ozv7rDU+n08nTpzQkSNHVFxcXG7N1q1bz9hzcnKyJkyYcOEvGgAABIyACVWJiYnatGmTvvjiC7/5Q4YMsX5u2bKlGjZsqK5du2rnzp26/vrrL3ebfkaPHq2kpCTrsc/nU3R0tIMdAQCASyUgQtWwYcO0ZMkSrV69Wo0aNTprbVxcnCRpx44duv766xUZGVnmU3o5OTmSpMjISOu/pfNOrwkLC1NoaKiCg4MVHBxcbk3pMsrjdrvldrsr9iIBAEBAq9TXVBljNGzYMP3973/XZ599ptjY2HM+JysrS5LUsGFDSVJ8fLy+++47v0/ppaWlKSwsTM2aNbNq0tPT/ZaTlpam+Ph4SVJISIjatm3rV1NSUqL09HSrBgAAVG2V+khVYmKi3nvvPS1evFi1atWyroEKDw9XaGiodu7cqffee0933XWX6tWrp40bN2rEiBHq1KmTWrVqJUnq1q2bmjVrpocffliTJk1Sdna2xowZo8TEROso0lNPPaVZs2bphRde0OOPP67PPvtMH374oZYuXWr1kpSUpIEDB+q2225T+/btNX36dB0/flyPPfbY5d8wAACg8jGVmKRyp3nz5hljjNm7d6/p1KmTqVu3rnG73eaGG24wI0eONLm5uX7L2b17t+nRo4cJDQ019evXN88//7wpKiryq1mxYoVp06aNCQkJMdddd521jtO9/vrrpnHjxiYkJMS0b9/efPnll+f1enJzc42kMv0BCGx5eXmmY8eOpmPHjiYvL8/pdgDYrKLv3wF1n6pAx32qgCsT96kCrmxX5H2qAAAAKitCFQAAgA0IVQAAADYgVAEAANiAUAUAAGADQhUAAIANCFUAAAA2IFQBAADYgFAFAABgA0IVAACADQhVAAAANqjmdAMAcDGMMcrPz3e0h9PX73QvkuTxeORyuZxuA6hyCFUAAlp+fr71ZcaVQa9evZxugS91BhzC6T8AAAAbcKQKQEDzeDxKTU11tAdjjAoKCiRJbrfb8VNvHo/H0fUDVRWhCkBAc7lcleJUV40aNZxuAYDDOP0HAABgA0IVAACADQhVAAAANiBUAQAA2IBQBQAAYANCFQAAgA0IVQAAADYgVAEAANiAUAUAAGADQhUAAIANCFUAAAA2IFQBAADYgFAFAABgg2pON1CVGGMkST6fz+FOAABARZW+b5e+j58Joeoy+vnnnyVJ0dHRDncCAADO188//6zw8PAzjrvMuWIXbFNSUqL9+/erVq1acrlcTrcT0Hw+n6Kjo/Xjjz8qLCzM6XYA9klUOuyT9jHG6Oeff1ZUVJSCgs585RRHqi6joKAgNWrUyOk2rihhYWH8skClwj6JyoZ90h5nO0JVigvVAQAAbECoAgAAsEHw+PHjxzvdBHAhgoODdeedd6paNc5io3Jgn0Rlwz55eXGhOgAAgA04/QcAAGADQhUAAIANCFUAAAA2IFQBZzB//nzVrl3b6TZwmf3y3338+PFq06aNX8348eMVEREhl8ulRYsWXdJ+rr32Wk2fPv2SrgOV25133qnhw4df9HIeffRR9e7d+7Ksq6oiVOGiPProo3K5XJo4caLf/EWLFl22u8b/85//VI0aNfTee+/5zS8pKdGvfvUr3X///ZelD1QO2dnZeuaZZ3TdddfJ7XYrOjpad999t9LT0y9oeb/5zW/8nrtlyxZNmDBBf/rTn3TgwAH16NHDrtbLtX79eg0ZMuSSrgPOK/1d+stpx44dtq1jxowZmj9/vm3LQ1mEKlw0j8ejV199VUeOHHFk/TfddJMmTpyoZ555RgcOHLDmT5kyRT/88IPmzJlz3sssKiqys0VcJrt371bbtm312WefafLkyfruu++0fPlydenSRYmJiRe0zKuuukr16tWzHu/cuVOS1KtXL0VGRsrtdl/Qciu6j1199dWqUaPGBa0DgaV79+46cOCA3xQbG3vRyy0uLlZJSYnCw8M5+n6JEapw0RISEhQZGank5OSz1n3xxRfq2LGjQkNDFR0drWeffVbHjx+XJM2aNUstWrSwakuPdJ0eiBISEjRmzJhyl/3MM8+odevWGjx4sCRp69atGjt2rN58803VrVtXL730kho1aiS32602bdpo+fLl1nN3794tl8ulDz74QJ07d5bH41FKSkqZdRw6dEi33Xab7r33XhUUFFR8A+Gyefrpp+VyufTVV1+pT58+uummm9S8eXMlJSXpyy+/lCRNnTpVLVu2VM2aNRUdHa2nn35ax44dO+MyTz/9N378eN19992STn3tVOnR2JKSkgvax0pPx7z22mtq2LCh6tWrp8TERL/A9cvTf+fbPwKH2+1WZGSk3xQcHFym7siRI3rkkUdUp04d1ahRQz169ND27dut8dJT2B9//LGaNWsmt9utvXv3ljn9d/z4cT3yyCO66qqr1LBhQ02ZMqXMuv7yl7/otttuU61atRQZGakHH3xQBw8evDQb4ApAqMJFCw4O1h/+8Ae9/vrr2rdvX7k1O3fuVPfu3dWnTx9t3LhRH3zwgb744gsNGzZMktS5c2d9//33OnTokCRp1apVql+/vlauXCnp1F/1GRkZuvPOO8tdvsvl0rx58/T555/rrbfe0qOPPqp+/frpnnvu0YwZMzRlyhS99tpr2rhxo7xer+655x6/X0KS9Nvf/lbPPfectmzZIq/X6zf2448/qmPHjmrRooX++te/XvDRCVw6P/30k5YvX67ExETVrFmzzHjpX+hBQUGaOXOmNm/erAULFuizzz7TCy+8UKF1/OY3v9G8efMkyTqSIOmi9rEVK1Zo586dWrFihRYsWKD58+ef9RTNxfSPK8Ojjz6qr7/+Wh9//LEyMjJkjNFdd93lF8bz8vL06quv6u2339bmzZvVoEGDMssZOXKkVq1apcWLF+uTTz7RypUrtWHDBr+aoqIivfzyy/r222+1aNEi7d69W48++uilfomBywAXYeDAgaZXr17GGGNuv/128/jjjxtjjPn73/9uTt+9Bg0aZIYMGeL33M8//9wEBQWZEydOmJKSElOvXj2zcOFCY4wxbdq0McnJySYyMtIYY8wXX3xhqlevbo4fP37WfubOnWuCgoJM48aNTW5urjHGmKioKPP73//er65du3bm6aefNsYYs2vXLiPJTJ8+3a9m3rx5Jjw83GzdutVER0ebZ5991pSUlJzX9sHls27dOiPJfPTRR+f1vIULF5p69epZj0v/3UuNGzfOtG7d2nr8y33bmAvfxwYOHGhiYmLMyZMnrXl9+/Y1DzzwgPU4JibGTJs2rcL9IzANHDjQBAcHm5o1a1rT/fffb4wxpnPnzua5554zxhjzz3/+00gya9assZ77r3/9y4SGhpoPP/zQGHNqH5ZksrKyyqyj9Pf1zz//bEJCQqznGGPM4cOHTWhoqLWu8qxfv95IMj///LM9L/wKw5Eq2ObVV1/VggULtGXLljJj3377rebPn6+rrrrKmrxer0pKSrRr1y65XC516tRJK1eu1NGjR/X999/r6aefVkFBgbZu3apVq1apXbt257y25LHHHlPDhg31zDPPKCwsTD6fT/v371eHDh386jp06FCmz9tuu63M8k6cOKGOHTvqvvvu04wZMy7bxfc4f6aCXw7x6aefqmvXrrrmmmtUq1YtPfzwwzp8+LDy8vIuaL0Xu481b97c7xRPw4YNz3p6xe7+UXl06dJFWVlZ1jRz5swyNVu2bFG1atUUFxdnzatXr56aNGnit7+FhISoVatWZ1zXzp07VVhY6LecunXrqkmTJn51mZmZuvvuu9W4cWPVqlVLnTt3liTt3bv3gl/nlYxQBdt06tRJXq9Xo0ePLjN27NgxPfnkk36/ML799ltt375d119/vaRTH+VduXKlPv/8c91yyy0KCwuzgtaqVaus/5nPpVq1ahf0PVflnTJyu91KSEjQkiVL9L//+7/nvUxcPjfeeKNcLpe2bt16xprdu3fr17/+tVq1aqW//e1vyszM1OzZsyVJhYWFl7zH8vax6tWr+z12uVwqKSkp9/lO949Lq2bNmrrhhhusqWHDhhe8rNDQ0Iv+I/D48ePyer0KCwtTSkqK1q9f///bu7uQqLYGjON/K81gkIyEsDLxI9LGEceoDCdTlJFITOwiqgshpQKpSKO60MAu1FKLSqKrDLMQKc0gApvQUCv6GiOyFMuC1KihLpTQyt6L3jPn9fVYVts+Ts8PvNh77b1cCxfD41pr9qa2thbQeBuLQpUYqqioiAsXLnDt2rUR561WKw8ePBjxgfHXj5eXF/D3vqqamhr33qkVK1Zw+fJlWlpaxtxP9Tk+Pj74j+lspwAABlFJREFU+/vT0tIy4nxLSwvh4eFfvH/SpElUVlYSHR1NfHw8PT09X90G+TFmzJiB3W6nvLzc/QWI//XmzRtu377N8PAwpaWlLF26lPnz53/33/R7x9jXmIj2y+8lLCyM9+/fc+PGDfc5l8vFo0ePvmq8BQcH4+npOaKe169f09HR4T5++PAhLpeLoqIibDYbCxYs0Cb1L1CoEkNFRESwfv36UdPWu3btorW1lezsbJxOJ52dnZw/f969UR3AYrHg6+vL6dOnR4Squro6BgcHRy2vjNfOnTspLi6murqaR48esXv3bpxOJ9u2bRvX/ZMnT6aqqorIyEgSEhLo6+v7pnbIxCsvL+fDhw8sXryYs2fP0tnZSXt7O4cPHyYmJoaQkBDevXvHkSNHePz4MZWVld/0yI3/971jbLwmqv3y+wgNDSU1NZWsrCyam5tpa2tjw4YNzJ49m9TU1HHXYzKZ2LhxIzt37uTKlSvcv3+fjIwMJk36OxYEBATg5eXlHm/19fXs27dvIrr1r6FQJYYrKCgYtXxhsVhoamqio6MDm81GVFQU+fn5+Pv7u6/x8PDAZrPh4eFBbGys+z4fHx8WLVr0j0sn47F161Z27NhBTk4OERERXLp0ifr6ekJDQ8ddx5QpUzhz5gwLFy4kISFB/639ooKCgrhz5w7x8fHk5ORgNptJSkrC4XBw7NgxIiMjKSsro7i4GLPZTFVV1RcfBTIeRoyx8Zio9svv5cSJE0RHR7Nq1SpiYmL4+PEjFy9eHLWU/CUHDhzAZrORkpJCYmIisbGxREdHu8v9/PyoqKigpqaG8PBwioqKKCkpMbo7/yoeH8e7u1NERERExqSZKhEREREDKFSJiIiIGEChSkRERMQAClUiIiIiBlCoEhERETGAQpWIiIiIARSqRERERAygUCUiIiJiAIUqERHg5cuXbNmyhYCAAKZOncqsWbOw2+3ud/p5eHhQV1f31fUGBgZy6NAho5srIr+gKT+7ASIiv4L09HSGhoY4efIkQUFBvHjxAofDgcvl+tlNE5HfhF5TIyJ/vDdv3uDr60tjYyNxcXGjygMDA3n69Kn7eN68eXR3d9PV1cWOHTu4fv06AwMDhIWFUVhYSGJiIvDpheBNTU0j6vrrI7e5uZk9e/Zw69YtZs6cSVpaGoWFhd/8jksR+fm0/CcifzyTyYTJZKKuro7BwcFR5Tdv3gQ+vci2t7fXfdzf38/KlStxOBzcvXuX5ORkUlJSePbsGQDnzp1jzpw5FBQU0NvbS29vLwBdXV0kJyeTnp7OvXv3qK6uprm5mezs7B/UYxGZCJqpEhEBzp49S1ZWFm/fvsVqtRIXF8fatWuxWCzApz1VtbW1rF69+rP1mM1mNm/e7A5IgYGBbN++ne3bt7uvyczMZPLkyRw/ftx9rrm5mbi4OAYGBvD29p6AHorIRNNMlYgIn/ZU9fT0UF9fT3JyMo2NjVitVioqKsa8p7+/n9zcXMLCwpg+fTomk4n29nb3TNVY2traqKiocM+QmUwm7HY7w8PDPHnyxOCeiciPoo3qIiL/5e3tTVJSEklJSeTl5ZGZmcnevXvJyMj4x+tzc3NpaGigpKSEkJAQpk2bxpo1axgaGvrs7+nv72fTpk1s3bp1VFlAQIARXRGRn0ChSkRkDOHh4e7HKHh6evLhw4cR5S0tLWRkZJCWlgZ8Ckvd3d0jrvHy8hp1n9Vq5cGDB4SEhExc40Xkh9Pyn4j88VwuFwkJCZw6dYp79+7x5MkTampq2L9/P6mpqcCnvVEOh4O+vj5ev34NQGhoKOfOncPpdNLW1sa6desYHh4eUXdgYCBXr17l+fPnvHr1CoBdu3bR2tpKdnY2TqeTzs5Ozp8/r43qIr85hSoR+eOZTCaWLFnCwYMHWb58OWazmby8PLKysjh69CgApaWlNDQ0MHfuXKKiogAoKyvD19eXZcuWkZKSgt1ux2q1jqi7oKCA7u5ugoOD8fPzA8BisdDU1ERHRwc2m42oqCjy8/Px9/f/sR0XEUPp238iIiIiBtBMlYiIiIgBFKpEREREDKBQJSIiImIAhSoRERERAyhUiYiIiBhAoUpERETEAApVIiIiIgZQqBIRERExgEKViIiIiAEUqkREREQMoFAlIiIiYgCFKhERERED/AcUh5Srt7m7kwAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "mO4h9fdsntik",
        "outputId": "03384fe7-80f8-4b7d-d699-d54a226e12cc"
      },
      "source": [
        "sns.heatmap(df.isnull())"
      ],
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f026875bb70>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 556
        },
        "id": "E_GJaCoLn1fD",
        "outputId": "5b3a51f8-3dac-491b-b104-aaf04a28c889"
      },
      "source": [
        "sns.heatmap(df.corr(),annot=True)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f0266d23668>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "AFQXqtaisw8R"
      },
      "source": [
        "# 1. Regression with SK-Learn"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sNnh_SwABDGr"
      },
      "source": [
        "### First Up, Encode the Categorical Variable."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gwOtV0dbA2_S",
        "outputId": "adb8f8db-418b-4c8f-b697-53095a017a55"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['R&D Spend', 'Administration', 'Marketing Spend', 'State', 'Profit'], dtype='object')"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "enOU0OcgEO9y"
      },
      "source": [
        "state = pd.get_dummies(df['State'],drop_first=True)"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "r_QkRlY3EUwm",
        "outputId": "9843b234-ada7-4f3d-b286-135b695f5cfd"
      },
      "source": [
        "df = df.drop('State',axis=1)\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>192261.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>191792.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>182901.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>166187.94</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend     Profit\n",
              "0  165349.20       136897.80        471784.10  192261.83\n",
              "1  162597.70       151377.59        443898.53  191792.06\n",
              "2  153441.51       101145.55        407934.54  191050.39\n",
              "3  144372.41       118671.85        383199.62  182901.99\n",
              "4  142107.34        91391.77        366168.42  166187.94"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "c5cnxpjeEcfj",
        "outputId": "01d8e76b-b6ac-460d-e61a-47cf96ac7e1f"
      },
      "source": [
        "df = pd.concat([df,state],axis=1)\n",
        "\n",
        "df.head()"
      ],
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>Profit</th>\n",
              "      <th>Florida</th>\n",
              "      <th>New York</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>192261.83</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>191792.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>191050.39</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>182901.99</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>166187.94</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend     Profit  Florida  New York\n",
              "0  165349.20       136897.80        471784.10  192261.83        0         1\n",
              "1  162597.70       151377.59        443898.53  191792.06        0         0\n",
              "2  153441.51       101145.55        407934.54  191050.39        1         0\n",
              "3  144372.41       118671.85        383199.62  182901.99        0         1\n",
              "4  142107.34        91391.77        366168.42  166187.94        1         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gU96D-HtEztW"
      },
      "source": [
        "X = df.drop('Profit',axis=1)\n",
        "\n",
        "y = df['Profit']"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xJE7_zNCE6yX"
      },
      "source": [
        "#Now do the Train Test Split. \n",
        "from sklearn.model_selection import train_test_split"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x37w9ctmFZPe"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "toSCtenjFlQm"
      },
      "source": [
        "#Creating and Fitting a linear Regression model. "
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wW4dmjiyFq2N",
        "outputId": "cb160ad5-94b0-475e-e9b2-b190db95b47b"
      },
      "source": [
        "from sklearn.linear_model import LinearRegression\n",
        "\n",
        "model = LinearRegression()\n",
        "\n",
        "model.fit(X_train,y_train)"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPfc9oGnF2PN"
      },
      "source": [
        "#Now Let's PRedict"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "F9l3XQzWF71y"
      },
      "source": [
        "yp = model.predict(X_test)"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BFm4wY32F-f-",
        "outputId": "0dfd2f93-9b65-40d6-ae2f-58cfc0561b2b"
      },
      "source": [
        "print(f'yp:{yp},\\n\\ny:{list(y_test)}')"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "yp:[126180.71023786  84282.04182273 100725.40834863  47648.04135995\n",
            " 129168.62389027  52171.29866289 110656.92317467 100967.73992208],\n",
            "\n",
            "y:[134307.35, 81005.76, 99937.59, 64926.08, 125370.37, 35673.41, 105733.54, 107404.34]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HsX3kR6LGBJU"
      },
      "source": [
        "#Now Let's See Model Metrics"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8QZwDCtsGVaF"
      },
      "source": [
        "from sklearn.metrics import r2_score, mean_squared_error"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qyI0d-3iGc7Y",
        "outputId": "da82aa8e-1ef4-422c-a6e8-2d8c43769a25"
      },
      "source": [
        "r2_score(yp,y_test)"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8902685933171746"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "_reTaZmWGfZO",
        "outputId": "8b9d9130-eb02-457f-b907-d6fb7678a7ad"
      },
      "source": [
        "np.sqrt(mean_squared_error(yp,y_test))"
      ],
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9540.72952398428"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "FLBpPJLCGogi",
        "outputId": "40b6e00f-e4e8-4172-bc08-61075c457910"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 45,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>Profit</th>\n",
              "      <th>Florida</th>\n",
              "      <th>New York</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>192261.83</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>191792.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>191050.39</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>182901.99</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>166187.94</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend     Profit  Florida  New York\n",
              "0  165349.20       136897.80        471784.10  192261.83        0         1\n",
              "1  162597.70       151377.59        443898.53  191792.06        0         0\n",
              "2  153441.51       101145.55        407934.54  191050.39        1         0\n",
              "3  144372.41       118671.85        383199.62  182901.99        0         1\n",
              "4  142107.34        91391.77        366168.42  166187.94        1         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 45
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0Uy5AmGtHcUq"
      },
      "source": [
        "### 2. Importance of Feature Engineering on Model Performance."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yjme7ADuJYW2"
      },
      "source": [
        "A. Dropping one of the highly correlated Features. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nBuW2VUBGrlq"
      },
      "source": [
        "# sns.heatmap(df.corr(),annot=True)\n",
        "# This shows that Marketing Spend and R&D spend is 0.72 correlated.\n",
        "# Also, R&D spend is 0.97 correlated to the Output. So let's pick only that."
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "I4b-zGw2HlDT",
        "outputId": "b70c22b2-a8f0-45cd-abbc-2d60f813867d"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>Profit</th>\n",
              "      <th>Florida</th>\n",
              "      <th>New York</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>192261.83</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>191792.06</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>191050.39</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>182901.99</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>166187.94</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend     Profit  Florida  New York\n",
              "0  165349.20       136897.80        471784.10  192261.83        0         1\n",
              "1  162597.70       151377.59        443898.53  191792.06        0         0\n",
              "2  153441.51       101145.55        407934.54  191050.39        1         0\n",
              "3  144372.41       118671.85        383199.62  182901.99        0         1\n",
              "4  142107.34        91391.77        366168.42  166187.94        1         0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V95SzuOIIElU"
      },
      "source": [
        "X = df.drop(['Profit','Marketing Spend'],axis=1)"
      ],
      "execution_count": 53,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1PyBuqHDIQGT"
      },
      "source": [
        "y = df['Profit']"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SiwH6-TgIR_n"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3GuxwdCCIXI6"
      },
      "source": [
        "lm = LinearRegression()"
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SyzxfdmAIb_0",
        "outputId": "33cfec96-f641-4fd0-bbf2-ba854c1aa84a"
      },
      "source": [
        "lm.fit(X_train,y_train)"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 57
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Qc6Ez5LEIf-e"
      },
      "source": [
        "yp1 = lm.predict(X_test)"
      ],
      "execution_count": 58,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lCsWH8kiIjlQ",
        "outputId": "bf8b57b8-ba5e-4d45-e243-d5d022be56b1"
      },
      "source": [
        "r2_score(yp1,y_test)"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8634632607122159"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 59
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FhlibLMFInhL",
        "outputId": "297a1442-9500-4b9b-938a-83687992dc21"
      },
      "source": [
        "mean_squared_error(yp1,y_test)**0.5"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "10272.391075995296"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 60
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BT6dcDw7Jd5l"
      },
      "source": [
        "B. Dropping the categorical feature. "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1vAdFQ2RI1Mz"
      },
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/krishnaik06/Multiple-Linear-Regression/master/50_Startups.csv')"
      ],
      "execution_count": 61,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "84QeEw-vJz8K"
      },
      "source": [
        "# df.head()\n",
        "df = df.drop('State',axis=1)"
      ],
      "execution_count": 63,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "gszqE29fJ1KW",
        "outputId": "521c7025-8c5d-4cd9-8cd5-ebaaffe0354c"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>R&amp;D Spend</th>\n",
              "      <th>Administration</th>\n",
              "      <th>Marketing Spend</th>\n",
              "      <th>Profit</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>165349.20</td>\n",
              "      <td>136897.80</td>\n",
              "      <td>471784.10</td>\n",
              "      <td>192261.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>162597.70</td>\n",
              "      <td>151377.59</td>\n",
              "      <td>443898.53</td>\n",
              "      <td>191792.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>153441.51</td>\n",
              "      <td>101145.55</td>\n",
              "      <td>407934.54</td>\n",
              "      <td>191050.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>144372.41</td>\n",
              "      <td>118671.85</td>\n",
              "      <td>383199.62</td>\n",
              "      <td>182901.99</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>142107.34</td>\n",
              "      <td>91391.77</td>\n",
              "      <td>366168.42</td>\n",
              "      <td>166187.94</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   R&D Spend  Administration  Marketing Spend     Profit\n",
              "0  165349.20       136897.80        471784.10  192261.83\n",
              "1  162597.70       151377.59        443898.53  191792.06\n",
              "2  153441.51       101145.55        407934.54  191050.39\n",
              "3  144372.41       118671.85        383199.62  182901.99\n",
              "4  142107.34        91391.77        366168.42  166187.94"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "P_G6OMa9J7gK"
      },
      "source": [
        "X = df.drop('Profit',axis=1)\n",
        "y = df['Profit']"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PzNhM0EDKDiN"
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.15, random_state=42)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ySqLtxC6KS8N"
      },
      "source": [
        "lm2 = LinearRegression()"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zCpXe_RQKU0t",
        "outputId": "3cefbadd-9ee5-4855-e769-642d5a7c82db"
      },
      "source": [
        "lm2.fit(X_train,y_train)"
      ],
      "execution_count": 68,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression(copy_X=True, fit_intercept=True, n_jobs=None, normalize=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 68
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PW4nZm2PKX2R"
      },
      "source": [
        "yp2 = lm2.predict(X_test)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VmSHEEtJKabP",
        "outputId": "31ddc3a1-1087-4313-bc37-c0d2a925f9b6"
      },
      "source": [
        "r2_score(yp2,y_test)"
      ],
      "execution_count": 70,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.8954281371406251"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 70
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z8HAWEhFKeU3",
        "outputId": "4843d49a-7a88-4a4b-ea3e-fd76cf021ad2"
      },
      "source": [
        "mean_squared_error(yp2,y_test)**0.5"
      ],
      "execution_count": 71,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "9375.740208690147"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 71
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "idNJnsATKkH-"
      },
      "source": [
        "#Compared to a previous error of 9500 somethin\n",
        "#hence this categorical feature is not important it seems."
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Pbx0KBl2KyWj"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}